Complex multitask compressive sensing using Laplace priors

被引：3

作者：

Zhang, Qilei ^{[1
]}

Dong, Zhen ^{[1
]}

Zhang, Yongsheng ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Elect Sci, Changsha 410073, Peoples R China

来源：

ELECTRONICS LETTERS | 2021年 / 57卷 / 25期

基金：

中国国家自然科学基金;

关键词：

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61771478; 61601008);

D O I：

10.1049/ell2.12331

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex-valued problems. To overcome this limitation, this letter extends the existing real-valued BCS framework to the complex-valued BCS framework. Within this framework, the multitask learning setting, where L tasks are statistically interrelated and share the same prior, is considered. It is verified by numerical examples that the developed complex multitask compressive sensing (CMCS) algorithm is more accurate and effective than the existing algorithms for the complex sparse signal reconstructions

引用

页码：998 / 1000

页数：3

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